In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn.feature_selection as fs
In [3]:
df=pd.read_csv(r'train.csv')
df.shape
Out[3]:
In [7]:
df.dtypes,df.describe()
Out[7]:
In [8]:
df.isnull().sum()
Out[8]:
In [10]:
df['class'].value_counts().plot(kind='bar')
Out[10]:
In [11]:
df.corr()
Out[11]:
In [13]:
df.corr()['class'].plot(kind='bar')
Out[13]:
In [14]:
df.nunique()
Out[14]:
In [19]:
age_bins=pd.cut(df.age,bins=10)
In [21]:
df['age_bins']=age_bins.cat.codes
In [22]:
df.corr()['class'].plot(kind='bar')
Out[22]:
In [24]:
cont_df=df.select_dtypes('float64')
In [25]:
cont_df.plot()
Out[25]:
In [58]:
for col in cont_df.columns:
fig=plt.figure(figsize=(20,10))
plt.scatter(cont_df[col].index,cont_df[col],c=df['class'])
plt.title(col)
plt.show()
In [61]:
plt.hist(one['chest'])
Out[61]:
In [65]:
one,zero=cont_df[df['class']==1].copy(),cont_df[df['class']==0].copy()
for col in cont_df.columns:
fig=plt.figure(figsize=(20,10))
plt.hist(one[col],alpha=0.5,color='red')
plt.hist(zero[col],alpha=0.5,color='green')
plt.title(col)
plt.show()
In [4]:
cat_df=df.select_dtypes('int64')
In [36]:
cat_df.groupby('class').agg('count')
Out[36]:
In [37]:
cat_df.columns
Out[37]:
In [43]:
cat_df.groupby(['class','sex','fasting_blood_sugar','resting_electrocardiographic_results','exercise_induced_angina','slope','number_of_major_vessels','thal']).agg('count')
Out[43]:
In [52]:
cat_df.groupby(['class','sex','fasting_blood_sugar','resting_electrocardiographic_results','exercise_induced_angina','slope','number_of_major_vessels','thal']).agg('count').plot(kind='bar')
plt.xticks(rotation='vertical')
plt.show()
In [49]:
cat_df.groupby(['class','sex','fasting_blood_sugar','resting_electrocardiographic_results']).agg('count').plot(kind='bar')
plt.xticks(rotation='vertical')
plt.show()
In [8]:
cat_df.columns[1:-1]
Out[8]:
In [10]:
ss=cat_df.groupby(['class','slope'])['ID'].count()
In [9]:
for col in cat_df.columns[1:-1]:
ss=cat_df.groupby(['class',col])['ID'].count()
fig, ax = plt.subplots()
ax.barh(list(range(ss.shape[0])),ss.values)
for i, v in enumerate(ss):
#ax.barh(ss.index,ss.values)
ax.text(v + 3, i + .25, str(v), color='blue', fontweight='bold')
plt.ylabel(ss.index.values)
plt.title(col)
plt.show()
In [29]:
import seaborn as sns
In [9]:
sns.countplot(cat_df['slope'])
Out[9]:
Feature Engineering with Model building¶
In [13]:
import xgboost as xgb
from xgboost import plot_importance, plot_tree
from sklearn.model_selection import train_test_split
In [12]:
clf=xgb.XGBClassifier()
In [19]:
df.iloc[:,1:-1].columns,df.iloc[:,-1].name
Out[19]:
In [21]:
X_train, X_test, y_train, y_test=train_test_split(df.iloc[:,1:-1],df.iloc[:,-1])
In [22]:
clf.fit(X_train,y_train)
Out[22]:
In [23]:
pred=clf.predict(X_test)
In [30]:
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test,pred)
sns.heatmap(cm)
Out[30]:
In [31]:
plot_importance(clf)
Out[31]:
In [32]:
plot_tree(clf)
